Cognitive analysis of security data with signal flow-based graph exploration

ABSTRACT

This disclosure provides for a signal flow analysis-based exploration of security knowledge represented in a graph structure comprising nodes and edges. “Conductance” values are associated to each of a set of edges. Each node has an associated “toxicity” value representing a degree of maliciousness associated with the node. The conductance value associated with an edge is a function of at least the toxicity values of the nodes to which the edge is incident. A signal flow analysis is conducted with respect to an input node representing an observable associated with an offense. The flow analysis seeks to identify a subset of the nodes that, based on their conductance values, are reached by flow of a signal representing a threat, wherein signal flow over a path in the graph continues until a signal threshold is met. Based on the analysis, nodes within the subset are designated as hypothesis nodes for further examination.

BACKGROUND Technical Field

This disclosure relates generally to cybersecurity offense analytics.

Background of the Related Art

Today's networks are larger and more complex than ever before, andprotecting them against malicious activity is a never-ending task.Organizations seeking to safeguard their intellectual property, protecttheir customer identities, avoid business disruptions, and the like,need to do more than just monitor logs and network flow data; indeed,many organizations create millions, or even billions, of events per day,and distilling that data down to a short list of priority offenses canbe daunting.

Known security products include Security Incident and Event Management(SIEM) solutions, which are built upon rule-based mechanisms to evaluateobserved security events. SIEM systems and methods collect, normalizeand correlate available network data. One such security intelligenceproduct of this type is IBM® QRadar® STEM, which provides a set ofplatform technologies that inspect network flow data to find andclassify valid hosts and servers (assets) on the network, tracking theapplications, protocols, services and ports they use. The productcollects, stores and analyzes this data, and it performs real-time eventcorrelation for use in threat detection and compliance reporting andauditing. Using this platform, billions of events and flows cantherefore be reduced and prioritized into a handful of actionableoffenses, according to their business impact. While SIEM-basedapproaches provide significant advantages, the rules are either hardcoded or parameterized with a threat feed with concrete indicators ofcompromise (IoCs). Thus, typically these solutions are able to detectonly known threats, but for unknown threats, e.g., detected by means ofa behavior based rule, are unable to identify root cause and assist thesecurity analyst. Moreover, these systems can present implementationchallenges, as they often rely on manual curation of any semi-structuredand unstructured threat feeds, i.e., natural language text, by means ofsecurity professionals reading threat advisories and extracting IoCs.

Security Operations Center (SOC) analysts who use such systems areconfronted with a large number of offenses every day. The majority oftheir time is spent to understand and analyze these offenses, confirmtheir validity, find related information, and attempt to findappropriate actions to resolve them. Typically, SOC analysts attempt tofind relevant cybersecurity intelligence reports and/or vulnerabilityreports for the target offenses from various data sources. To this end,mostly they use web search engines to query and manually browse threatand security intelligence Internet services. Given the widely-disparateinformation sources, an analyst often is faced with many, oftenconflicting, data sources and hypotheses to read and process to draw aconclusion.

Presently, there are no automated systems or tools to do search,filtering, and prioritization of hypotheses for security offenses. Thesubject matter of this disclosure addresses this need.

BRIEF SUMMARY

This disclosure provides a technique for a signal flow analysis-basedexploration of security knowledge represented in a graph structure.Analytics over that graph structure can reveal potential causalrelationships between security events and offenses that are reportedfrom a security system (e.g., a SIEM). To this end, in a firstembodiment, the disclosure provides for an automated method forcognitive analysis of security data using a knowledge graph comprisingnodes and edges. The method begins by associating “conductance” valuesto each of a set of edges in at least a portion of the knowledge graph.Each node in the portion of the knowledge graph has an associated“toxicity” value representing a degree of maliciousness associated withthe node. The conductance value associated with a given edge of the setof edges is a function of at least the toxicity values of the nodes towhich the given edge is incident. The method then continues with respectto an input node representing an observable associated with an offenseand, in particular, by conducting a signal flow analysis. The purpose ofthe signal flow analysis is to identify a subset of the nodes that,based on their conductance values, are reached by flow of a signalrepresenting a threat, wherein flow of the signal over a given path inthe knowledge graph continues until a signal threshold is met. Based onthe signal flow analysis, the method associates nodes within the subsetas hypothesis nodes. The hypothesis nodes represent a hypothesis to beexamined to attempt to identify information in the knowledge graphrepresenting a causal relationship that led to the offense.

According to a second aspect of this disclosure, an apparatus forcognitive analysis of security data using a knowledge graph comprisingnodes and edges is described. The apparatus comprises a processor, andcomputer memory holding computer program instructions executed by theprocessor to perform a set of operations such as described above.

According to a third aspect of this disclosure, a computer programproduct in a non-transitory computer readable medium for use in a dataprocessing system for cognitive analysis of security data using aknowledge graph comprising nodes and edges is described. The computerprogram product holds computer program instructions executed in the dataprocessing system and operative to perform operations such as describedabove.

The foregoing has outlined some of the more pertinent features of thesubject matter. These features should be construed to be merelyillustrative. Many other beneficial results can be attained by applyingthe disclosed subject matter in a different manner or by modifying thesubject matter as will be described.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawings, in which:

FIG. 1 depicts an exemplary block diagram of a distributed dataprocessing environment in which exemplary aspects of the illustrativeembodiments may be implemented;

FIG. 2 is an exemplary block diagram of a data processing system inwhich exemplary aspects of the illustrative embodiments may beimplemented;

FIG. 3 illustrates a security intelligence platform in which thetechniques of this disclosure may be practiced;

FIG. 4 depicts a high level process flow of the cognitive analysistechnique of this disclosure;

FIG. 5 depicts the cognitive analysis technique in additional detail;and

FIG. 6 depicts how an offense context graph is augmented using asecurity knowledge graph according to this disclosure;

FIG. 7 depicts a representative example scenario and, in particular, apartial set of findings that are output from the SIEM about a potentialoffense;

FIG. 8 depicts a representative offense context graph that is generatedfrom the findings;

FIG. 9 depicts a representative offense graph after enrichment withnodes and relationships from the security knowledge graph;

FIG. 10 depicts a close-up view of the enriched offense graph;

FIG. 11 depicts how a knowledge graph finding (the subgraph) is mergedinto the offense context graph and then scored and pruned according tothe technique of this disclosure;

FIG. 12 depicts a representative implementation of a subgraphexploration algorithm according to this disclosure; and

FIG. 13 depicts a subgraph generated by the exploration.

DETAILED DESCRIPTION OF AN ILLUSTRATIVE EMBODIMENT

With reference now to the drawings and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments of the disclosure may beimplemented. It should be appreciated that FIGS. 1-2 are only exemplaryand are not intended to assert or imply any limitation with regard tothe environments in which aspects or embodiments of the disclosedsubject matter may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

With reference now to the drawings, FIG. 1 depicts a pictorialrepresentation of an exemplary distributed data processing system inwhich aspects of the illustrative embodiments may be implemented.Distributed data processing system 100 may include a network ofcomputers in which aspects of the illustrative embodiments may beimplemented. The distributed data processing system 100 contains atleast one network 102, which is the medium used to provide communicationlinks between various devices and computers connected together withindistributed data processing system 100. The network 102 may includeconnections, such as wire, wireless communication links, or fiber opticcables.

In the depicted example, server 104 and server 106 are connected tonetwork 102 along with storage unit 108. In addition, clients 110, 112,and 114 are also connected to network 102. These clients 110, 112, and114 may be, for example, personal computers, network computers, or thelike. In the depicted example, server 104 provides data, such as bootfiles, operating system images, and applications to the clients 110,112, and 114. Clients 110, 112, and 114 are clients to server 104 in thedepicted example. Distributed data processing system 100 may includeadditional servers, clients, and other devices not shown.

In the depicted example, distributed data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, the distributed data processing system 100 may also beimplemented to include a number of different types of networks, such asfor example, an intranet, a local area network (LAN), a wide areanetwork (WAN), or the like. As stated above, FIG. 1 is intended as anexample, not as an architectural limitation for different embodiments ofthe disclosed subject matter, and therefore, the particular elementsshown in FIG. 1 should not be considered limiting with regard to theenvironments in which the illustrative embodiments of the presentinvention may be implemented.

With reference now to FIG. 2, a block diagram of an exemplary dataprocessing system is shown in which aspects of the illustrativeembodiments may be implemented. Data processing system 200 is an exampleof a computer, such as client 110 in FIG. 1, in which computer usablecode or instructions implementing the processes for illustrativeembodiments of the disclosure may be located.

With reference now to FIG. 2, a block diagram of a data processingsystem is shown in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as server104 or client 110 in FIG. 1, in which computer-usable program code orinstructions implementing the processes may be located for theillustrative embodiments. In this illustrative example, data processingsystem 200 includes communications fabric 202, which providescommunications between processor unit 204, memory 206, persistentstorage 208, communications unit 210, input/output (I/O) unit 212, anddisplay 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor (SMP) system containing multiple processors of the sametype.

Memory 206 and persistent storage 208 are examples of storage devices. Astorage device is any piece of hardware that is capable of storinginformation either on a temporary basis and/or a permanent basis. Memory206, in these examples, may be, for example, a random access memory orany other suitable volatile or non-volatile storage device. Persistentstorage 208 may take various forms depending on the particularimplementation. For example, persistent storage 208 may contain one ormore components or devices. For example, persistent storage 208 may be ahard drive, a flash memory, a rewritable optical disk, a rewritablemagnetic tape, or some combination of the above. The media used bypersistent storage 208 also may be removable. For example, a removablehard drive may be used for persistent storage 208.

Communications unit 210, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard and mouse. Further, input/output unit 212 may sendoutput to a printer. Display 214 provides a mechanism to displayinformation to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 208. These instructions may be loaded intomemory 206 for execution by processor unit 204. The processes of thedifferent embodiments may be performed by processor unit 204 usingcomputer implemented instructions, which may be located in a memory,such as memory 206. These instructions are referred to as program code,computer-usable program code, or computer-readable program code that maybe read and executed by a processor in processor unit 204. The programcode in the different embodiments may be embodied on different physicalor tangible computer-readable media, such as memory 206 or persistentstorage 208.

Program code 216 is located in a functional form on computer-readablemedia 218 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 216 and computer-readable media 218 form computerprogram product 220 in these examples. In one example, computer-readablemedia 218 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer-readable media 218 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer-readable media 218 is also referred to ascomputer-recordable storage media. In some instances,computer-recordable media 218 may not be removable.

Alternatively, program code 216 may be transferred to data processingsystem 200 from computer-readable media 218 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples. Thecomputer-readable media also may take the form of non-tangible media,such as communications links or wireless transmissions containing theprogram code. The different components illustrated for data processingsystem 200 are not meant to provide architectural limitations to themanner in which different embodiments may be implemented. The differentillustrative embodiments may be implemented in a data processing systemincluding components in addition to or in place of those illustrated fordata processing system 200. Other components shown in FIG. 2 can bevaried from the illustrative examples shown. As one example, a storagedevice in data processing system 200 is any hardware apparatus that maystore data. Memory 206, persistent storage 208, and computer-readablemedia 218 are examples of storage devices in a tangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

Computer program code for carrying out operations of the presentinvention may be written in any combination of one or more programminglanguages, including an object-oriented programming language such asJava™, Smalltalk, C++ or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Those of ordinary skill in the art will appreciate that the hardware inFIGS. 1-2 may vary depending on the implementation. Other internalhardware or peripheral devices, such as flash memory, equivalentnon-volatile memory, or optical disk drives and the like, may be used inaddition to or in place of the hardware depicted in FIGS. 1-2. Also, theprocesses of the illustrative embodiments may be applied to amultiprocessor data processing system, other than the SMP systemmentioned previously, without departing from the spirit and scope of thedisclosed subject matter.

As will be seen, the techniques described herein may operate inconjunction within the standard client-server paradigm such asillustrated in FIG. 1 in which client machines communicate with anInternet-accessible Web-based portal executing on a set of one or moremachines. End users operate Internet-connectable devices (e.g., desktopcomputers, notebook computers, Internet-enabled mobile devices, or thelike) that are capable of accessing and interacting with the portal.Typically, each client or server machine is a data processing systemsuch as illustrated in FIG. 2 comprising hardware and software, andthese entities communicate with one another over a network, such as theInternet, an intranet, an extranet, a private network, or any othercommunications medium or link. A data processing system typicallyincludes one or more processors, an operating system, one or moreapplications, and one or more utilities. The applications on the dataprocessing system provide native support for Web services including,without limitation, support for HTTP, SOAP, XML, WSDL, UDDI, and WSFL,among others. Information regarding SOAP, WSDL, UDDI and WSFL isavailable from the World Wide Web Consortium (W3C), which is responsiblefor developing and maintaining these standards; further informationregarding HTTP and XML is available from Internet Engineering Task Force(IETF). Familiarity with these standards is presumed.

Security Intelligence Platform with Incident Forensics

A representative security intelligence platform in which the techniquesof this disclosure may be practiced is illustrated in FIG. 3. Generally,the platform provides search-driven data exploration, sessionreconstruction, and forensics intelligence to assist security incidentinvestigations. In pertinent part, the platform 300 comprises a set ofpacket capture appliances 302, an incident forensics module appliance304, a distributed database 306, and a security intelligence console308. The packet capture and module appliances are configured as networkappliances, or they may be configured as virtual appliances. The packetcapture appliances 302 are operative to capture packets off the network(using known packet capture (pcap) application programming interfaces(APIs) or other known techniques), and to provide such data (e.g.,real-time log event and network flow) to the distributed database 306,where the data is stored and available for analysis by the forensicsmodule 304 and the security intelligence console 308. A packet captureappliance operates in a session-oriented manner, capturing all packetsin a flow, and indexing metadata and payloads to enable fastsearch-driven data exploration. The database 306 provides a forensicsrepository, which distributed and heterogeneous data sets comprising theinformation collected by the packet capture appliances. The console 308provides a web- or cloud-accessible user interface (UI) that exposes a“Forensics” dashboard tab to facilitate an incident investigationworkflow by an investigator. Using the dashboard, an investigatorselects a security incident. The incident forensics module 304 retrievesall the packets (including metadata, payloads, etc.) for a selectedsecurity incident and reconstructs the session for analysis. Arepresentative commercial product that implements an incidentinvestigation workflow of this type is IBM® Security QRadar® IncidentForensics V7.2.3 (or higher). Using this platform, an investigatorsearches across the distributed and heterogeneous data sets stored inthe database, and receives a unified search results list. The searchresults may be merged in a grid, and they can be visualized in a“digital impression” tool so that the user can explore relationshipsbetween identities.

In particular, a typical incident forensics investigation to extractrelevant data from network traffic and documents in the forensicrepository is now described. According to this approach, the platformenables a simple, high-level approach of searching and bookmarking manyrecords at first, and then enables the investigator to focus on thebookmarked records to identify a final set of records. In a typicalworkflow, an investigator determines which material is relevant. He orshe then uses that material to prove a hypothesis or “case” to developnew leads that can be followed up by using other methods in an existingcase. Typically, the investigator focuses his or her investigationthrough course-grained actions at first, and then proceeds to fine-tunethose findings into a relevant final result set. The bottom portion ofFIG. 3 illustrates this basic workflow. Visualization and analysis toolsin the platform may then be used to manually and automatically assessthe results for relevance. The relevant records can be printed,exported, or submitted processing.

As noted above, the platform console provides a user interface tofacilitate this workflow. Thus, for example, the platform provides asearch results page as a default page on an interface display tab.Investigators use the search results to search for and access documents.The investigator can use other tools to further the investigation. Oneof these tools is a digital impression tool. A digital impression is acompiled set of associations and relationships that identify an identitytrail. Digital impressions reconstruct network relationships to helpreveal the identity of an attacking entity, how it communicates, andwhat it communicates with. Known entities or persons that are found inthe network traffic and documents are automatically tagged. Theforensics incident module 304 is operative to correlate taggedidentifiers that interacted with each other to produce a digitalimpression. The collection relationships in a digital impression reportrepresent a continuously-collected electronic presence that isassociated with an attacker, or a network-related entity, or any digitalimpression metadata term. Using the tool, investigators can click anytagged digital impression identifier that is associated with a document.The resulting digital impression report is then listed in tabular formatand is organized by identifier type.

Generalizing, a digital impression reconstructs network relationships tohelp the investigator identify an attacking entity and other entitiesthat it communicates with. A security intelligence platform includes aforensics incident module that is operative to correlate taggedidentifiers that interacted with each other to produce a digitalimpression. The collection relationships in a digital impression reportrepresent a continuously-collected electronic presence that isassociated with an attacker, or a network-related entity, or any digitalimpression metadata term. Using the tool, investigators can click anytagged digital impression identifier that is associated with a document.The resulting digital impression report is then listed in tabular formatand is organized by identifier type.

Typically, an appliance for use in the above-described system isimplemented is implemented as a network-connected, non-display device.For example, appliances built purposely for performing traditionalmiddleware service oriented architecture (SOA) functions are prevalentacross certain computer environments. SOA middleware appliances maysimplify, help secure or accelerate XML and Web services deploymentswhile extending an existing SOA infrastructure across an enterprise. Theutilization of middleware-purposed hardware and a lightweight middlewarestack can address the performance burden experienced by conventionalsoftware solutions. In addition, the appliance form-factor provides asecure, consumable packaging for implementing middleware SOA functions.One particular advantage that these types of devices provide is tooffload processing from back-end systems. A network appliance of thistype typically is a rack-mounted device. The device includes physicalsecurity that enables the appliance to serve as a secure vault forsensitive information. Typically, the appliance is manufactured,pre-loaded with software, and then deployed within or in associationwith an enterprise or other network operating environment;alternatively, the box may be positioned locally and then provisionedwith standard or customized middleware virtual images that can besecurely deployed and managed, e.g., within a private or an on premisecloud computing environment. The appliance may include hardware andfirmware cryptographic support, possibly to encrypt data on hard disk.No users, including administrative users, can access any data onphysical disk. In particular, preferably the operating system (e.g.,Linux) locks down the root account and does not provide a command shell,and the user does not have file system access. Typically, the appliancedoes not include a display device, a CD or other optical drive, or anyUSB, Firewire or other ports to enable devices to be connected thereto.It is designed to be a sealed and secure environment with limitedaccessibility and then only be authenticated and authorized individuals.

An appliance of this type can facilitate Security Information EventManagement (SIEM). For example, and as noted above, IBM® SecurityQRadar® STEM is an enterprise solution that includes packet data captureappliances that may be configured as appliances of this type. Such adevice is operative, for example, to capture real-time Layer 4 networkflow data from which Layer 7 application payloads may then be analyzed,e.g., using deep packet inspection and other technologies. It providessituational awareness and compliance support using a combination offlow-based network knowledge, security event correlation, andasset-based vulnerability assessment. In a basic QRadar STEMinstallation, the system such as shown in FIG. 3 is configured tocollect event and flow data, and generate reports. As noted, a user(e.g., an SOC analyst) can investigate offenses to determine the rootcause of a network issue.

Generalizing, Security Information and Event Management (SIEM) toolsprovide a range of services for analyzing, managing, monitoring, andreporting on IT security events and vulnerabilities. Such servicestypically include collection of events regarding monitored accesses andunexpected occurrences across the data network, and analyzing them in acorrelative context to determine their contribution to profiledhigher-order security events. They may also include analysis of firewallconfigurations, network topology and connection visualization tools forviewing current and potential network traffic patterns, correlation ofasset vulnerabilities with network configuration and traffic to identifyactive attack paths and high-risk assets, and support of policycompliance monitoring of network traffic, topology and vulnerabilityexposures. Some SIEM tools have the ability to build up a topology ofmanaged network devices such as routers, firewalls, and switches basedon a transformational analysis of device configurations processedthrough a common network information model. The result is a locationalorganization which can be used for simulations of security threats,operational analyses of firewall filters, and other applications. Theprimary device criteria, however, are entirely network- andnetwork-configuration based. While there are a number of ways to launcha discovery capability for managed assets/systems, and while containmentin the user interface is semi-automatically managed (that is, anapproach through the user interface that allows for semi-automated,human-input-based placements with the topology, and its display andformatting, being data-driven based upon the discovery of both initialconfigurations and changes/deletions in the underlying network), nothingis provided in terms of placement analytics that produce fully-automatedplacement analyses and suggestions.

Cognitive Offense Analysis Using Contextual Data and Knowledge Graphs

With the above as background, the techniques of this disclosure are nowdescribed.

The basic approach of this disclosure involves processing security eventdata in association with a cybersecurity knowledge graph (“KG”). Thecybersecurity knowledge graph is derived one or more data sources andincludes a set of nodes, and a set of edges. The method preferably isautomated and begins upon receipt of information from a security system(e.g., a STEM) representing an offense. Based on the offense type,context data about the offense is extracted, and an initial offensecontext graph is built. The initial offense context graph typicallycomprises a set of nodes, and a set of edges, with an edge representinga relationship between a pair of nodes in the set. At least one of theset of nodes in the offense context graph is a root node representing anoffending entity that is determined as a cause of the offense. Theinitial offense context graph also includes one or more activity nodesconnected to the root node either directly or through one or more othernodes of the set, wherein at least one activity node has associatedtherewith data representing an observable. The root node and its one ormore activity nodes associated therewith (and the observables) representa context for the offense. According to the method, the knowledge graphand potentially other data sources are then examined to further refinethe initial offense context graph.

In particular, preferably the knowledge graph is explored by locatingthe observables (identified in the initial offense graph) in theknowledge graph. Based on the located observables and their connectionsbeing associated with one or more known malicious entities asrepresented in the knowledge graph, one or more subgraphs of theknowledge graph are then generated. A subgraph typically has ahypothesis (about the offense) associated therewith. Using a hypothesis,the security system (or other data source) is then queried to attempt toobtain one or more additional observables (i.e. evidence) supporting thehypothesis. Then, a refined offense context graph is created, preferablyby merging the initial offense context graph, the one or more sub-graphsderived from the knowledge graph exploration, and the additionalobservables mined from the one or more hypotheses. The resulting refinedoffense context graph is then provided (e.g., to a SOC analyst) forfurther analysis.

An offense context graph that has been refined in this manner, namely,by incorporating one or more subgraphs derived from the knowledge graphas well as additional observables mined from examining the subgraphhypotheses, provides for a refined graph that reveals potential causalrelationships more readily, or otherwise provides information thatreveals which parts of the graph might best be prioritized for furtheranalysis. The approach herein thus greatly simplifies the furtheranalysis and corrective tasks that must then be undertaken to addressthe root cause of the offense.

With reference now to FIG. 4, a high level process flow of the techniqueof this disclosure is provided The routine begins at step 400 withoffense extraction and analysis. In this step, an offense is extractedfrom a SIEM system, such as IBM QRadar, for deep investigation.Typically, a detected offense may include many different entities, suchas offense types, fired rules, user names, and involved indicators ofcompromise.

At step 402, the process continues with offense context extraction,enrichment and data mining. Here, offense context is extracted andenriched based on various information or factors such as, withoutlimitation, time, an offense type, and a direction. This operationtypically involves data mining around the offense to find potentiallyrelated events. The process then continues at step 404 to build anoffense context graph, preferably with the offending entity as thecenter node and contextual information gradually connected to the centernode and its children. Examples of contextual information can berepresented by activity nodes in the graph. Typically, an activitycomprises one or more observables, which are then connected to therespective activity, or directly to the center node.

The process then continues at step 406. In particular, at this step aknowledge graph is explored, preferably using a set of observablesextracted from the offense context graph. This exploration stepidentifies related and relevant pieces of information or entitiesavailable from the knowledge graph. A primary goal in this operation isto find out how strongly the input observables are related to maliciousentities in the knowledge graph. If the event related entities arestrong malicious indicators, a hypothesis (represented by a subgraph inthe knowledge graph) is generated. The process then continues at step408. At this step, the resulting subgraph (generated in step 406) ismapped into the original offense context graph and scored. To reinforcethe hypothesis (represented by the subgraph), additional evidence may beobtained (and built into the offense context graph) by querying localSTEM data for the presence of activities that are related to thehypothesis that is returned by the KG exploration in step 406.Additional findings as part of the hypothesis scoring may also be usedto extend the offense context graph further and/or to trigger newknowledge graph explorations. Thus, step 408 represents anevidence-based scoring of the threat hypothesis.

The process then continues at step 410 with an offense investigation. Atthis point, the offense hypothesis includes the original offense IOCs(indicators of compromise), knowledge graph enrichment, evidence, andscores. The extended offense context graph is then provided to the SOCanalyst (user) for offense investigation. The SOC user reviews thehypothesis that has been weighted in the manner described, and can thenchoose the right hypothesis that explains the given offense. There maybe multiple hypotheses.

If additional or further exploration and more evidence are needed tomake a decision, the SOC user can elect to nodes or edges in the offensecontext graph and repeat steps 406 and 408 as needed. This iteration isdepicted in the drawing. This completes the high level process flow.

FIG. 5 depicts a modeling diagram showing the various entities involvedin the technique and their interactions. As depicted, these entitiesinclude the SOC user 500, the SIEM system 502, the (offense) contextgraph 504, a knowledge graph 506, and a maintenance entity 508. Viewingthe interactions from top to bottom, the knowledge graph 506 may beupdated with new data/records 510 periodically; this operation is shownas an off-line operation (above the dotted line). The remainder of thefigure depicts the process flow referenced above. Thus, the new offense505 is identified by the SIEM system 502 and used together with theoffense details 510 and data mining 512 to generate the context graph504 via the offense extraction and analysis 514 and context graphbuilding 516 operations. Once built, the knowledge graph 506 is explored518 to identify one or more subgraphs. The evidence-based threathypothesis scoring uses the subgraphs at operation 520, and the processmay iterate (operation 522) as previously described. After evidencevalidation and IOC mining 524, the offense investigation 526 is thencarried out, typically by the SOC user 500.

FIG. 6 depicts the offense context graph 600 augmented by the knowledgegraph 602. In general, the offense context graph 600 depicts localkinetics, e.g., events and intelligence related to an offense, e.g.,SIEM offense data, log events and flows, and such information preferablyis augmented from the information derived from the knowledge graph 602.The knowledge graph is global in nature and scope, as it preferablydepicts external cyber security and threat intelligence, cyber securityconcepts, and the like. Typically, the knowledge graph is informed bycombining multiple structured and unstructured data sources. As shown,the offense context graph is centered around a root node 604 that haschild nodes 606 within the “offense” 605. The “offense context” 607includes still other nodes of relevance. There may also be a set ofdevice activities 609 that include relevant device nodes 608. Asdepicted by the arrow 610, augmenting the context graph 600 using theknowledge graph 602 examines whether there is any path (such as one ormore of paths 611, 613 or 615) from a node in the set of offense contextnodes 607 to a node in the set of device activities 609 that passesthrough one or more nodes of the knowledge graph 602 (to which a threatactivity is attached)? In the example shown, there is one or more suchpaths (611, 613 and 615), and the relevant subgraph 617 in the knowledgegraph thus is captured and used to augment the offense context graph.

Thus, in the approach, details of an offense are extracted from a STEMsystem, such as QRadar. The details typically include offense types,rules, categories, source and destination IP addresses, and user names.For example, an offense may be a malware category offense that indicatesthat malicious software is detected on a machine. Accordingly,activities of the machine around the offense need to be examined todetermine infection vectors and potential data leakage. Of course, thenature of the activities that will need to be investigated will dependon the nature of the offense.

According to a further aspect of the approach herein, offense contextrelated to an identified offense is then extracted and enricheddepending on various factors, such as time, an offense type, and adirection. For example, if an offense type is a source IP, system andnetwork activities of the same source IP (which may or may not becaptured at other offenses) may then be collected. This collectedcontext depicts potential casual relationships among events, and thisinformation then provides a basis for investigation of provenance andconsequences of an offense, e.g., Markov modeling to learn theirdependencies. Of course, the nature of the offense context extractionand enrichment also depends on the nature of the offense.

From the contextual data extracted (as described above), an initialoffense “context graph” 600 in FIG. 6 is built, preferably depending onoffense types, such that a main offense source becomes a root 604 of anoffense context graph, and offense details are then linked togetheraround the root node. As noted above, the initial context graphpreferably is then enriched and, in particular, by correlating localcontext, to further identify potential causal relationships amongevents. This helps analysts perform deep, more fine-grainedinvestigation of provenance and consequences of the offense.

In a preferred embodiment, provenance context preferably is extracted byidentifying other offenses wherein the offense source is a target, e.g.,an exploit target. Similarly, consequence context is extracted,preferably by finding other offenses wherein the offense source also isa source, e.g., a stepping stone. Similarly, consequence context isextracted by finding other offenses. Thus, this graph typically containsthe offending entity (e.g., computer system, user, etc.) as the center(root) node of the graph, and contextual information is graduallyconnected to the node and its children. The result is the offensecontext 607 in FIG. 6. Examples of contextual information will depend onthe nature of the offense; such information can be represented byactivity nodes that include, without limitation, network activity, useractivity, system activity, application activity, and so forth.Preferably, an activity comprises one or more observables, which arethen connected to the respective activity nodes or directly to thecenter node. Further, the context graph can be extended with additionalnodes representing information that does not directly relate to theoriginal offense. For example, and by means of data mining (e.g.,behavior-based anomaly detection, sequence mining, rule-based dataextraction, and the like) of security-related events in temporalvicinity to the offense, additional activities of interest can beextracted and added to the context graph. This operation is representedin the graph by device activities 606.

Thus, in the approach as outlined so far, details of an offense areextracted from a SIEM system. The details include (but are not limitedto) offense types, rules, categories, source and destination IPs, anduser names. An initial offense context graph is built depending onoffense types, such that the main offense source becomes the root of anoffense context graph and offense details are linked together around theroot node. The initial context graph is then enriched by correlatinglocal context to further identify potential casual relationships amongevents, which helps analysts perform deep investigation of provenanceand consequences of the offense. Provenance context is extracted byidentifying other offenses where the offense source is a target, e.g.,an exploit target. Similarly, consequence context is extracted byfinding other offenses where the offense target is a source, e.g., astepping stone. The enriched (and potentially dense) offense contextgraph is then pruned to highlight critical offense context for the SOCanalyst's benefit. Typically, pruning is applied based on severalmetrics, such as weight, relevance, and time. For example, it may bedesirable to assign weight to each event detail based on offense rulesand categories to thereby indicate key features contributing to anoffense.

Once the initial offense context graph is built, preferably that contextgraph is further enriched, validated and/or augmented based oninformation derived from a cybersecurity knowledge graph (KG) 602, whichpreferably is a source of domain knowledge. The knowledge graph, likethe initial offense context graph, comprises nodes and edges. Thecybersecurity knowledge graph can be constructed in several ways. In oneembodiment, one or more domain experts build a KG manually. In anotherembodiment, a KG 602 is built automatically or semi-automatically, e.g.,from structured and unstructured data sources. As noted above, thecontext extraction and analysis processes provide a list of observablesrelated to the given offense. According to this operation, theobservables preferably are then enriched using the in-depth domainknowledge in the KG. This enrichment (or knowledge graph exploration) isnow described.

In particular, this knowledge graph (KG) enrichment operation can bedone in several different ways. In one approach, enrichment involvesbuilding sub-graphs related to the observables. To this end, the systemlocates the observables in the KG and discovers the connections amongthem. This discovery may yield one or more subgraphs (such as 617 inFIG. 6) showing the relationships of the given observables with otherrelated security objects such as observables and threats. Thesesubgraphs can provide a broader view on the given offense.

In another enrichment scenario, a SOC analyst can perform the queryknowledge graph (KG) exploration step receives a set of observables,such as IP, URL, and files hashes, extracted from the SIEM offense. Thisexploration step seeks to identify all related and relevant pieces ofinformation or entities available in the knowledge graph. The main goalis to find out how strongly the input observables are related tomalicious entities in the knowledge graph. Some of the related entitiescan be strong malicious indicators, and thus a hypothesis about theoffense can be generated. The related malicious entities might bestrongly related among themselves, which also creates a hypothesis.Generalizing, an output of this step is a set of one or more hypotheses,which are consumed during the evidence-based threat hypothesis scoringoperation where they are evaluated against local SIEM data. Preferably,and as noted above, the extraction of related entities is performed bytraversing the knowledge graph, preferably starting from the inputobservables and extracting the subgraph. In general, unconstrainedsubgraph extraction may result in a very large and noise graph. Thus,and as will be further described below, preferably one or more traversalalgorithms that focus on finding different types of related informationby pruning less relevant entities from the result may be deployed. Oneor more of these pruning algorithms may be run serially, in parallel, orotherwise. In addition, and will also be further described, wherepossible coefficients of the graph entities are precomputed to enhancethe efficiency of the graph traversal.

The following describes additional details of the evidence-based threathypothesis scoring. Preferably, the knowledge graph exploration stepreturns a subgraph of observables, along with one or more annotationsassociated with the hypotheses. This subgraph preferably is then mappedinto the original context graph. To reinforce the hypotheses, it may bedesirable to build further relevant evidence, e.g., by querying localSIEM data for the presence of activities that are related to thehypotheses returned by the knowledge graph exploration. These activitiesmay not have been flagged before by a simple rule-based offense monitor.This operation thus builds a merged graph that includes input from threesources, the original context graph, the knowledge graph explorationsubgraph, and the additional observables queried for building theevidence for the hypotheses.

As also described, the final operation typically is offenseinvestigation. Based on the prior operations described, the offensehypotheses now include the original offense IOCs, knowledge graphenrichment and supporting evidences, and their scores. This extendedgraph then is provided to an SOC analyst for an offense investigation.The SOC analyst reviews the weighted hypotheses and chooses the righthypothesis that explains the given offense. The selection itself may beautomated, e.g., via machine learning. If further exploration and moreevidence are needed to make a decision, the SOC can choose the nodesand/or edges of interest in the hypothesis graphs, and then repeat theabove-described steps of knowledge graph exploration and evidence-basedthreat hypotheses scoring. During the hypothesis review process, the SOCmay learn new facts and insights about the offense and, thus, addadditional queries (e.g. observables or relationship) in a nextiteration. The SOC analyst thus can use this iterative knowledgeenrichment, evidence generation and hypothesis scoring to gain a deepunderstanding of the offense and actionable insights that may then beacted upon.

Thus, the basic notion of this approach is to use an autonomic mechanismto extract what is known about an offense (or attack), reason about theoffense based on generalized knowledge (as represented by the knowledgegraph), and thereby arrive at a most probable diagnosis about theoffense and how to address it.

FIG. 7 depicts a representative example scenario and, in particular, apartial set of findings that are output from the STEM about a potentialoffense (in this example, a ransom-ware exploit). FIG. 8 depicts arepresentative offense context graph that is generated from thefindings. Nodes in the graph indicate observable properties of theoffense events, including information such as IP addresses, domainnames, URLs, malware hashes. Edges represent semantic relationshipsbetween the nodes. FIG. 9 depicts a representative offense context graphafter enrichment with information from the security knowledge graph. Theenrichment may add multiple additional nodes to the existing offensecontext graph, which are deemed to be related to the initial offense'snodes. FIG. 10 depicts a close-up view of the enriched offense graph inFIG. 9, showing the relationship types in more detail. An edge marked as“LINKS” between a file and a URL means that the file was downloaded fromthe URL (download URL), and an edge marked as “CONNECT” means the filemade an HTTP request to URL (e.g., post-infection). FIG. 11 depicts howa KG-determined subgraph finding is merged into the offense graph andthen pruned and scored to identity that several activities of thisoffense all relate to a single attack family.

As described above, after generating the enriched offense context graph,preferably this graph is pruned, e.g., to reduce its size to a moremanageable level. The following provides additional details regardingpruning of the enriched offense context graph.

In particular, pruning can be accomplished in various ways including,without limitation, consolidating nodes that represent redundantinformation, removing nodes and edges that are found to be outside anypath from an input node (e.g., representing an observable associatedwith the offense under examination) to one or more nodes that are knownto be malicious, summarizing subgraphs to a higher-level abstraction,and hiding irrelevant intermediate nodes on paths. According to aparticular pruning technique, a metric (e.g., weight, relevance,distance, degree, toxicity, time, or the like) is applied to one or moreevents associated with the offense. Then, and based on one or more rules(and categories) that indicate key features and characteristics of theoffense, nodes are scored according to the metric(s), and nodes withscores below a threshold are removed. Additional information, such asnetwork structures and connectivity, and identity of high value assets,may also be leveraged to tune the metric(s). In addition, paths betweenthe root node and malicious nodes are determined, and nodes outside ofany such paths preferably are marked for removal. Clustering of the sametype of nodes (e.g., anti-virus signatures, file names, reputation, andURLs) connected to a given node is performed to summarize into arepresentative placeholder potentially redundant and overlappinginformation. In addition, subgraphs and intermediate nodes preferablyare analyzed for their semantic meaning and relevance and, whereappropriate, replaced by a summary node and/or removed. The result issometimes referred to herein as a pruned offense context graph (or apruned context graph).

Subgraph Exploration

The following provides additional details regarding a preferredtechnique to explore the knowledge graph as needed to facilitate adetermination of how strongly the input observables are related tomalicious entities in the graph. As previously described, the knowledgegraph (or given portions thereof) is assumed to comprise nodes(vertices) and edges between nodes. As previously described, aparticular offense that is received from the STEM typically includes oneor more observables. According to this aspect of the disclosure, it isdesired to “explore” the knowledge graph to determine how far (andthrough what nodes) a theoretical “threat intelligence signal” may flowthrough the knowledge graph starting from an input represented by thenode corresponding to the observable (e.g., an IP address), and possiblyend up at one or more “toxic” nodes representing known maliciousinformation (e.g., a node that might have contributed to the offense).This notion of signal flow analysis is sometimes referred to as subgraphexploration, as the result of the exploration typically identifies asubgraph of the knowledge graph. This nomenclature is not intended to belimiting. The threat intelligence signal in effect is a simulation, andthe nature and extent of this “flow” preferably is influenced asfollows.

In particular, preferably each node in the knowledge graph (or each nodeof a given subset) has a “toxicity” coefficient attached to it; thisvalue represents how malicious a node is. In addition, and according tothis approach, each edge in the knowledge graph has a “conductance”coefficient attached to it. Conductance refers to an extent by which theedge “dissipates” the threat intelligence signal that is assumed to flowthrough that edge. An edge with high conductance provides less signaldissipation than an edge with low conductance. A “conductance”coefficient typically depends on several factors, such as the type ofthe connection (between nodes), confidence and recency of the edgerelation, and, more importantly, the “toxicity” of the connected nodes.When an edge is incident to a node that has high toxicity, thecorresponding conductance for that edge is higher. When an edge isincient to a node that has lower or no toxicity, the correspondingconductance for that edge is lower. As the threat intelligence signal issimulated to flow through the knowledge graph (from the input noderepresenting the observable), it dissipates, eventually reaching athreshold. When that threshold is reached, the threat intelligencesignal is no longer considered to be flowing. At this point, there are aset of nodes and edges that have been traversed by the threatintelligence signal. Nodes with high toxicity values are then consideredto be hypothesis nodes for further investigation.

The particular values of the toxicity and conductance coefficients maybe configured in any convenient manner, but these values are set lessthan 1.0, as conductance values equal to or greater than one would notinhibit flow (thereby extending the exploration into further reaches ofthe knowledge graph indefinitely). In operation, preferably the nodecorresponding to the observable has a normalized value equal to 1.0, andthat value starts to dissipate as the flow is initiated outward acrossthe knowledge graph from the observable.

FIG. 12 depicts a process flow for an implementation of the pruningalgorithm. The process starts at step 1200 by initializing an inputobservable list with an initial signal. The flow then continues at step1202 to set the input list as a next list. At step 1204, a test isperformed to determine if the next list is empty. If so, the routinebranches to step 1206 to generate an edge-induced subgraph from anoutput list of edges. The routine then ends. If, however, the outcome ofthe test at step 1204 returns a negative response, the routine continuesat step 1208 to copy the next list to a current list. At step 1210, theroutine empties the next list. A test is then performed at step 1212 todetermine if any observable is in the current list. If the outcome ofthe test at step 1212 is negative, the routine returns to step 1204. If,however, the outcome of the test at step 1212 indicates that there is anobservable in the current list, the routine continues at step 1214.

At step 1214, the routine pops the observable node from the currentlist. The routine then continues at step 1216 to dissipate the signal toall its connected nodes. At step 1218, a test is performed to determinewhether the signal at the connected node is greater than a threshold. Ifthe outcome of the test at step 1218 is positive, the routine continuesat step 1220 to add the connecting edge to the output list. At step1222, the routine then adds the connecting node to the next list.Control then returns to step 1212, which step is also reached when theoutcome of the test at step 1218 is negative. This completes theprocessing.

FIG. 13 depicts this exploration process for the set of nodes depicted.The size of a particular node represents its signal flow value, withnodes 1300 and 1302 being the set of inputs (the observables taken fromthe offense) and each normalized to a value “1.” There is assumed to bea “signal cutoff” threshold for the graph; when this value is reachedalong any path, the signal is said to be dissipated and the flow stops.For this particular example, the threat intelligence signal flow isdepicted and includes a number of paths. Thus, for example, after theflow from input node 1300 to node 1304, and due to the conductance ofthe incident edge 1318, the signal cutoff threshold is reached and thesignal flow stopped (with respect to this path). In comparison, the flowfrom node 1302 (that also starts at 1.0, representing the 100% value ofthe signal) flows to node 1308, with the conductance of edge 1320causing some dissipation of the signal, but not enough to stop the flow.Thus, node 1308 is shown as being larger in size than node 1304. As alsodepicted, each of the input nodes 1300 and 1302 contribute the signal tothe node 1306 over respective edges 1322 and 1324. Due to the additivenature of the flow, the node 1306 is shown as larger than node 1308 and,once again, the flow can continue outward. As also depicted, the flowfrom node 1308 then continues through node 1312, and then node 1314,dissipating along the way, until once again the signal cutoff thresholdis reached; flow along this path then end. That said, the flow from node1306 is shown as continuing through 1310 and then onward to node 1316,which is this example is a node with high toxicity. Node 1316, togetherwith one or more other nodes depicted that are linked thereto and alsohave high toxicity, will then be returned as an edge-induced “subgraph”of the knowledge graph. These high toxicity nodes are sometimes referredto as hypothesis nodes, as they form a “hypothesis” (e.g., that node1316 is the cause of the observable 1300 or 1302).

According to a further feature of this technique, it is desired to“spread” toxicity values associated with the nodes across neighboringnodes to thereby influence the signal flow(s). In particular, and in apre-processing operation (i.e., before the above-described graphtraversal responsive to a query), preferably a toxicity value of aparticular node is spread to other neighboring nodes to ensure that theparticular node thereby has a greater ability to attract a signalflowing through the graph. As noted above, and given the impact toxicityhas on conductance of incident nodes, the higher the toxicity of theparticular node, the more it acts as a signal attractor. Thus, byspreading a node's toxicity to its neighboring nodes, the technique ofthis disclosure ensures that a threat intelligence signal will find itsway to a toxic node instead of dissipating before reaching that node.

Generalizing, a goal of the exploration technique is to implement atraversal algorithm that generates a useful KG subgraph, i.e., one thatextracts related entities while pruning less relevant entities from theresult. To this end, and has been described the traversal algorithmimplements the notion of a threat intelligence “signal” that is assumedto “flow” through the graph. Just like in an electrical circuit, theconductance coefficient for a particular edge through which that signalmight pass attenuates (i.e., dissipates) the signal. Stated another way,as the signal flows, it is thus attenuated (and dissipates). When theattenuation falls below a configurable threshold (the signal cutoff),exploration over that path stops.

In operation, and assuming the toxicity and conductance coefficients arein place, an edge-induced subgraph is generated with respect to each ofone or more observables in the offense by taking all the paths where thesignal flows. This is depicted above, as explained with respect to theexample scenario in FIG. 13. As also depicted there, multiple inputobservables (e.g., nodes 1300 and 1302) produce multiple signals. Inthis approach, preferably a resultant signal strength is computed fromthese signals while the overall intelligence signal flows and dissipatesthrough the knowledge graph. In this way, internal nodes that arerelated to multiple input nodes are identified for deeper explorationthan they would otherwise. To ensure stability, preferably signal valuesare separately tracked for each input observable on every node. If ahigher value signal arrives from the same input node but from adifferent (shorter) path, preferably the higher value replaces theexisting value. Preferably, the sum of all signal values at a node isused to check the signal stop threshold detection. By keeping allconductance coefficients less than 1.0 and separately tracking thesignals, the traversal is guaranteed to converge. As noted above, eachnode has a toxicity coefficient, and preferably this value is spreadindependently in the graph (as a pre-processing operation), therebyinfluencing neighboring nodes. Further, once the signal flow is carriedout, nodes with high value of “toxicity” then form the hypothesis nodesof the investigation.

As an additional variant, preferably entities that are not in the pathfrom any input nodes to the hypothesis nodes are pruned.

Thus, according to this embodiment, a method for building a contextgraph for a detected offense (which might include many differententities), further enriching the context graph by mining offensecontexts from different data sources (such as the knowledge graph, orother offenses), and pruning the enriched context graph to highlight thecritical areas in the offense context for the target offense so that theSOC analyst can turn his or her attention to the highlighted areasfirst. Stated another way, the exploration approach provides for asignal-flow-analysis-based algorithmic exploration of security knowledgerepresented in a graph structure.

The technique of this disclosure provides significant advantages. Thetechnique builds an enriched offense context graph that revealspotential causal relationships between security events and offenses,thereby helping the analyst comprehend an offense more thoroughly. Theapproach enables the analyst to prioritize which parts of the graph tobe investigated first, thereby leading to faster solution. The approachprovides security analysts with more comprehensive context from avariety of kinetics data imported into a SIEM system. For deep andefficient investigation, the described approach leverages acomprehensive set of rules, and it offers enriched relevant context ofan offense. The approach enables efficient mining of offense context(e.g., activities, device event details, offense rules and categories,etc.) and to provide a comprehensive context graph for follow-on deepinvestigation and analysis.

More generally, the approach herein provides for an enhanced data miningprocess on security data (e.g., a cybersecurity incident) to extractcontextual data related to the incident, and to translate thisinformation into a graph representation for investigation by a securityanalyst. The approach, being automated, is highly efficient, and itgreatly eases the workflow requirements for the SOC analyst.

The technique herein also provides for enhanced automated andintelligent investigation of a suspicious network offense so thatcorrective action may be taken. The nature of the corrective action isnot an aspect of the described methodology, and any known orlater-developed technologies and systems may be used for this purpose.

One of ordinary skill in the art will further appreciate that thetechnique herein automates the time-consuming and often difficultresearch and investigation process that has heretofore been the provinceof the security analyst. The approach retrieves knowledge about the IOCsusing a knowledge graph preferably extracted from public and/or privatestructured and unstructured data sources, and then extends thatknowledge even further, thereby greatly reducing the time necessary forthe analyst to determine cause and effect.

The approach herein is designed to be implemented in an automated mannerwithin or in association with a security system, such as a SIEM.

The knowledge graph may be a component of the system, or such a graphmay be used by the system.

The functionality described above may be implemented as a standaloneapproach, e.g., a software-based function executed by a processor, or itmay be available as a managed service (including as a web service via aSOAP/XML interface). The particular hardware and software implementationdetails described herein are merely for illustrative purposes are notmeant to limit the scope of the described subject matter.

More generally, computing devices within the context of the disclosedsubject matter are each a data processing system (such as shown in FIG.2) comprising hardware and software, and these entities communicate withone another over a network, such as the Internet, an intranet, anextranet, a private network, or any other communications medium or link.The applications on the data processing system provide native supportfor Web and other known services and protocols including, withoutlimitation, support for HTTP, FTP, SMTP, SOAP, XML, WSDL, UDDI, andWSFL, among others. Information regarding SOAP, WSDL, UDDI and WSFL isavailable from the World Wide Web Consortium (W3C), which is responsiblefor developing and maintaining these standards; further informationregarding HTTP, FTP, SMTP and XML is available from Internet EngineeringTask Force (IETF). Familiarity with these known standards and protocolsis presumed.

The scheme described herein may be implemented in or in conjunction withvarious server-side architectures including simple n-tier architectures,web portals, federated systems, and the like. The techniques herein maybe practiced in a loosely-coupled server (including a “cloud”-based)environment.

Still more generally, the subject matter described herein can take theform of an entirely hardware embodiment, an entirely software embodimentor an embodiment containing both hardware and software elements. In apreferred embodiment, the function is implemented in software, whichincludes but is not limited to firmware, resident software, microcode,and the like. Furthermore, as noted above, the identity context-basedaccess control functionality can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any apparatus thatcan contain or store the program for use by or in connection with theinstruction execution system, apparatus, or device. The medium can be anelectronic, magnetic, optical, electromagnetic, infrared, or asemiconductor system (or apparatus or device). Examples of acomputer-readable medium include a semiconductor or solid state memory,magnetic tape, a removable computer diskette, a random access memory(RAM), a read-only memory (ROM), a rigid magnetic disk and an opticaldisk. Current examples of optical disks include compact disk-read onlymemory (CD-ROM), compact disk-read/write (CD-R/W) and DVD. Thecomputer-readable medium is a tangible item.

The computer program product may be a product having programinstructions (or program code) to implement one or more of the describedfunctions. Those instructions or code may be stored in a computerreadable storage medium in a data processing system after beingdownloaded over a network from a remote data processing system. Or,those instructions or code may be stored in a computer readable storagemedium in a server data processing system and adapted to be downloadedover a network to a remote data processing system for use in a computerreadable storage medium within the remote system.

In a representative embodiment, the graph generation techniques areimplemented in a special purpose computer, preferably in softwareexecuted by one or more processors. The software is maintained in one ormore data stores or memories associated with the one or more processors,and the software may be implemented as one or more computer programs.Collectively, this special-purpose hardware and software comprises thefunctionality described above.

Further, any authentication or authorization functionality requiredherein may be implemented as an adjunct or extension to an existingaccess manager or policy management solution.

While the above describes a particular order of operations performed bycertain embodiments of the invention, it should be understood that suchorder is exemplary, as alternative embodiments may perform theoperations in a different order, combine certain operations, overlapcertain operations, or the like. References in the specification to agiven embodiment indicate that the embodiment described may include aparticular feature, structure, or characteristic, but every embodimentmay not necessarily include the particular feature, structure, orcharacteristic.

Finally, while given components of the system have been describedseparately, one of ordinary skill will appreciate that some of thefunctions may be combined or shared in given instructions, programsequences, code portions, and the like.

The techniques herein provide for improvements to another technology ortechnical field, namely, security incident and event management (SIEM)systems, as well as improvements to automation-based knowledgegraph-based analytics.

A refined offense context graph as described herein may be rendered forvisual display to the SOC analyst to facilitate the follow-on analysis.

The technique for graph exploration described herein may be generalizedand used for exploration of any arbitrary graph having nodes and edges,and wherein the nodes and edges are provided toxicity and conductancevalues to facilitate a signal flow analysis (relevant to the informationpresented in the graph) in the manner described above.

Having described our invention, what we claim is as follows:
 1. A methodfor cognitive analysis of security data using a knowledge graphcomprising nodes and edges, comprising: associating conductance valuesto each of a set of edges in at least a portion of the knowledge graph;with respect to an input node representing an observable associated withan offense, conducting a signal flow analysis to identify a subset ofthe nodes that, based on their conductance values, are reached by flowof a signal representing a threat, wherein flow of the signal over agiven path in the knowledge graph continues until a signal threshold ismet; and associating nodes within the subset as hypothesis nodes, thehypothesis nodes representing a hypothesis to be examined to attempt toidentify information in the knowledge graph representing a causalrelationship that led to the offense.
 2. The method as described inclaim 1 wherein each node in the portion of the knowledge graph has anassociated toxicity value representing a degree of maliciousnessassociated with the node.
 3. The method as described in claim 2 whereinthe conductance value associated with a given edge of the set of edgesis a function of at least the toxicity values of the nodes to which thegiven edge is incident.
 4. The method as described in claim 3 whereinthe conductance value associated with the given edge is also anadditional factor that is one of: an edge type, a confidence of an edgerelation, a recency of an edge relation, and a source or destinationnode.
 5. The method as described in claim 3 further including spreadinga toxicity value associated with a given node to one or more of itsneighboring nodes prior to conducting the signal flow analysis.
 6. Themethod as described in claim 1 wherein the nodes in the subset thatconstitute the hypothesis are nodes that toxicity values that, relativeto toxicity values of other nodes in the knowledge graph, are higher. 7.The method as described in claim 1 further including pruning nodes andedges that are not in any path from the input node to any node that is ahypothesis node.
 8. An apparatus for cognitive analysis of security datausing a knowledge graph comprising nodes and edges, comprising: aprocessor; computer memory holding computer program instructionsexecuted by the processor, the computer program instructions operativeto: associate conductance values to each of a set of edges in at least aportion of the knowledge graph; with respect to an input noderepresenting an observable associated with an offense, conduct a signalflow analysis to identify a subset of the nodes that, based on theirconductance values, are reached by flow of a signal representing athreat, wherein flow of the signal over a given path in the knowledgegraph continues until a signal threshold is met; and associate nodeswithin the subset as hypothesis nodes, the hypothesis nodes representinga hypothesis to be examined to attempt to identify information in theknowledge graph representing a causal relationship that led to theoffense.
 9. The apparatus as described in claim 8 wherein each node inthe portion of the knowledge graph has an associated toxicity valuerepresenting a degree of maliciousness associated with the node.
 10. Theapparatus as described in claim 9 wherein the conductance valueassociated with a given edge of the set of edges is a function of atleast the toxicity values of the nodes to which the given edge isincident.
 11. The apparatus as described in claim 8 wherein theconductance value associated with the given edge is also an additionalfactor that is one of: an edge type, a confidence of an edge relation, arecency of an edge relation, and a source or destination node.
 12. Theapparatus as described in claim 10 wherein the computer programinstructions are further operative to spread a toxicity value associatedwith a given node to one or more of its neighboring nodes prior toconducting the signal flow analysis.
 13. The apparatus as described inclaim 8 wherein the nodes in the subset that constitute the hypothesisare nodes that toxicity values that, relative to toxicity values ofother nodes in the knowledge graph, are higher.
 14. The apparatus asdescribed in claim 8 wherein the computer program instructions arefurther operation to prune nodes and edges that are not in any path fromthe input node to any node that is a hypothesis node.
 15. A computerprogram product in a non-transitory computer readable medium for use ina data processing system for cognitive analysis of security data using aknowledge graph comprising nodes and edges, the computer program productholding computer program instructions that, when executed by the dataprocessing system, are operative to: associate conductance values toeach of a set of edges in at least a portion of the knowledge graph;with respect to an input node representing an observable associated withan offense, conduct a signal flow analysis to identify a subset of thenodes that, based on their conductance values, are reached by flow of asignal representing a threat, wherein flow of the signal over a givenpath in the knowledge graph continues until a signal threshold is met;and associate nodes within the subset as hypothesis nodes, thehypothesis nodes representing a hypothesis to be examined to attempt toidentify information in the knowledge graph representing a causalrelationship that led to the offense.
 16. The computer program productas described in claim 15 wherein each node in the portion of theknowledge graph has an associated toxicity value representing a degreeof maliciousness associated with the node.
 17. The computer programproduct as described in claim 16 wherein the conductance valueassociated with a given edge of the set of edges is a function of atleast the toxicity values of the nodes to which the given edge isincident.
 18. The computer program product as described in claim 15wherein the conductance value associated with the given edge is also anadditional factor that is one of: an edge type, a confidence of an edgerelation, a recency of an edge relation, and a source or destinationnode.
 19. The computer program product as described in claim 17 whereinthe computer program instructions are further operative to spread atoxicity value associated with a given node to one or more of itsneighboring nodes prior to conducting the signal flow analysis.
 20. Thecomputer program product as described in claim 15 wherein the nodes inthe subset that constitute the hypothesis are nodes that toxicity valuesthat, relative to toxicity values of other nodes in the knowledge graph,are higher.
 21. The computer program product as described in claim 15wherein the computer program instructions are further operation to prunenodes and edges that are not in any path from the input node to any nodethat is a hypothesis node.